Tensor factorization toward precision medicine

Yuan Luo*, Fei Wang, Peter Szolovits

*Corresponding author for this work

Research output: Contribution to journalComment/debatepeer-review

31 Scopus citations


Precision medicine initiatives come amid the rapid growth in quantity and variety of biomedical data, which exceeds the capacity of matrix-oriented data representations and many current analysis algorithms. Tensor factorizations extend the matrix view to multiple modalities and support dimensionality reduction methods that identify latent groups of data for meaningful summarization of both features and instances. In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest that tensor applications could serve as an effective tool to enable frequent updating of medical knowledge based on the continually growing scientific and clinical evidence. We encourage extensive experimental studies to tackle challenges including design choice of factorizations, integrating temporality and algorithm scalability.

Original languageEnglish (US)
Pages (from-to)511-514
Number of pages4
JournalBriefings in Bioinformatics
Issue number3
StatePublished - May 1 2017


  • Biomedical data mining
  • Multiple data modalities
  • Precision medicine
  • Tensor factorization

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


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